B+-trees

Model of Computation Data stored on disk(s) Minimum transfer unit: a page = b bytes or B records (or block) N records -> N/B = n pages I/O complexity: in number of pages CPUMemory Disk

I/O complexity An ideal index has space O(N/B), update overhead O(1) or O(log B (N/B)) and search complexity O(a/B) or O(log B (N/B) + a/B) where a is the number of records in the answer But, sometimes CPU performance is also important… minimize cache misses -> don’t waste CPU cycles

B+-tree Records must be ordered over an attribute, SSN, Name, etc. Queries: exact match and range queries over the indexed attribute: “find the name of the student with ID= ” or “find all students with gpa between 3.00 and 3.5”

B+-tree:properties Insert/delete at log F (N/B) cost; keep tree height-balanced. (F = fanout) Minimum 50% occupancy (except for root). Each node contains d <= m <= 2d entries/pointers. Two types of nodes: index nodes and data nodes; each node is 1 page (disk based method)

Example Root

to keys to keysto keys to keys < 5757  k<8181  k<95 95  Index node

Data node To record with key 57 To record with key 81 To record with key 85 From non-leaf node to next leaf in sequence Struct { Key real; Pointr *long; } entry; Node entry[B];

Insertion Find correct leaf L. Put data entry onto L. If L has enough space, done! Else, must split L (into L and a new node L2) Redistribute entries evenly, copy up middle key. Insert index entry pointing to L2 into parent of L. This can happen recursively To split index node, redistribute entries evenly, but push up middle key. (Contrast with leaf splits.) Splits “grow” tree; root split increases height. Tree growth: gets wider or one level taller at top.

Deletion Start at root, find leaf L where entry belongs. Remove the entry. If L is at least half-full, done! If L has only d-1 entries, Try to re-distribute, borrowing from sibling (adjacent node with same parent as L). If re-distribution fails, merge L and sibling. If merge occurred, must delete entry (pointing to L or sibling) from parent of L. Merge could propagate to root, decreasing height.

Characteristics Optimal method for 1-d range queries: Space: O(N/B), Updates: O(log B (N/B)), Query:O(log B (N/B) + a/B) Space utilization: 67% for random input Original B-tree: index nodes store also pointers to records

Example Root Range[32, 160]

Other issues Internal node architecture [Lomet01]: Reduce the overhead of tree traversal. Prefix compression: In index nodes store only the prefix that differentiate consecutive sub-trees. Fanout is increased. Cache sensitive B+-tree Place keys in a way that reduces the cache faults during the binary search in each node. Eliminate pointers so a cache line contains more keys for comparison.

References [Lomet01]: David B. Lomet: The Evolution of Effective B-tree: Page Organization and Techniques: A Personal Account. SIGMOD Record 30(3): (2001)SIGMOD Record 30